Multi-model predictive control with local constraints based on model switching

Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （MLD） system and use it in model predictive control （MPC） in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control （MMPC） algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output（MIMO） simulated pH neutralization process.

Functionally Graded Cellular Structure Design Using the Subdomain Level Set Method with Local Volume Constraints

Functional graded cellular structure(FGCS)usually shows superiormechanical behaviorwith lowdensity and high stiffness.With the development of additivemanufacturing,functional graded cellular structure gains its popularity in industries.In this paper,a novel approach for designing functionally graded cellular structure is proposed based on a subdomain parameterized level set method(PLSM)under local volume constraints(LVC).In this method,a subdomain level set function is defined,parameterized and updated on each subdomain independently making the proposed approach much faster and more cost-effective.Additionally,the microstructures on arbitrary two adjacent subdomains can be connected perfectly without any additional constraint.Furthermore,the local volume constraint for each subdomain is applied by virtue of the augmented Lagrange multiplier method.Finally,several numerical examples are given to verify the correctness and effectiveness of the proposed approach in designing the functionally graded cellular structure.From the optimized results,it is also found that the number of local volume constraints has little influence on the convergence speed of the developed approach.

Efficient tracker based on sparse coding with Euclidean local structure-based constraint

Abstract:Sparse coding(SC)based visual tracking(l1-tracker)is gaining increasing attention,and many related algorithms are developed.In these algorithms,each candidate region is sparsely represented as a set of target templates.However,the structure connecting these candidate regions is usually ignored.Lu proposed an NLSSC-tracker with non-local self-similarity sparse coding to address this issue,which has a high computational cost.In this study,we propose an Euclidean local-structure constraint based sparse coding tracker with a smoothed Euclidean local structure.With this tracker,the optimization procedure is transformed to a small-scale l1-optimization problem,significantly reducing the computational cost.Extensive experimental results on visual tracking demonstrate the eectiveness and efficiency of the proposed algorithm.

Influence of local environmental target constraints on enterprise OFDI

By collecting the environmental target constraints in the annual government work report of prefecture-level cities,this paper studies its impact on local enterprises’OFDI.The main conclusions are as follows:(1)Both direct and indirect constraints have a significant positive impact on enterprises’OFDI,and the degree of direct constraints is stronger than that of indirect constraints.(2)Environmental target constraints of local governments will affect corporate OFDI behavior by affecting production costs,local economic development level and local openness,and(3)There are regional heterogeneity and investment type heterogeneity in the impact of environmental goal constraints on corporate OFDI.The suggestions are as follows:government departments can appropriately strengthen the constraint intensity of environmental targets and formulate specific restraint rules for industries with different levels of pollution,so as to effectively make use of the technology spillover effects brought about by OFDI to promote the upgrading of domestic industrial structure.When making OFDI,enterprises should clarify the investment motivation,strengthen corporate social responsibility,and make use of technology spillover effect to promote the upgrading and development of home country industry while being beneficial to their own development.

Distributed cooperative localization for sparse communication network with multi-locating messages

In cooperative localization with sparse communication networks, an agent maybe only receives part of locating messages from the others. It is difficult for the receiver to utilize the part instead of global knowledge. Under the extended Kalman filtering, the utilization of the locating message is maximized by two aspects： the locating message generating and multi-locating messages fusing. For the former, the covariance upper-bound technique, by introducing amplification coefficients, is employed to remove the dependency of locating messages on the global knowledge. For the latter, an optimization model is setup; the covariance matrix determinant of the receiver＇s state estimate, expressed as a function of the amplification coefficients, is selected as the optimization criterion, under linear constraints on the amplification coefficient characteristics and the communication connectivity. Using the optimization solution, the local optimal state of the receiver agent is obtained by the weighting fusion. Simulation with seven agents is shown to evaluate the effectiveness of the proposed algorithm.

Generating Parametric G^n Blending Surface with Some Constraints

This paper presents a method of generating a parametric G^n blending surfacebased on reparameterizing the partial surface patches in the base surfaces on the basis of ErichHartmann method. This method is expressed as follows Firstly, the partial region near contact curvesin both base surfaces is reparameterized. The contact curves are used as the boundaries of thereparameterized partial region respectively. The reparameterized partial region in two base surfacesis called the reparameterized local base surfaces. Then the parametric G^n blending surface isgenerated by a linear combination of the reparameterized local base surface patches depending on oneof the common parameters. Therefore, generating a Parametric G^n Blending Surface between two basesurfaces is translated into generating a Parametric G^n Blending Surface between the tworeparameterized local base surfaces. This paper illustrates the method to generate the G^n blendingsurface with some constraints by generating a G^2 blending surface between the aerofoil and the bodyof a missile with the constraints of the forward and rear fringe curves. When the G^n blendingsurface with some constraints is generated, the partial region near contact curves in both basesurfaces is reparameterized, and the scale factors, offset, balance factor and thumb weight aredefined by meeting the constraints through using an optimization method. Then the parametric G^nblending surface is generated by the linear combination of the reparameterized local base surfacepatches. The shape of the blending surface can be adjusted by changing the size of thereparameterized local base surface patches.

A Novel Vision Localization Method of Automated Micro-Polishing Robot

Based on photogrammetry technology,a novel localization method of micro-polishing robot,which is restricted within certain working space,is presented in this paper.On the basis of pinhole camera model,a new mathematical model of vision localization of automated polishing robot is established.The vision localization is based on the distance-constraints of feature points.The method to solve the mathematical model is discussed.According to the characteristics of gray image,an adaptive method of automatic threshold selection based on connected components is presented.The center coordinate of the feature image point is resolved by bilinear interpolation gray square weighted algorithm.Finally,the mathematical model of testing system is verified by global localization test.The experimental results show that the vision localization system in working space has high precision.

政府债务预算硬约束与地方财政可持续性提升

地方政府债务风险的不断积累已成为地方财政可持续性的最大阻碍。以新《预算法》实施后,地方政府债务管理的“开前门”和“关后门”两个阶段改革为准自然实验,分析了政府债务预算硬约束对地方财政可持续性的影响。研究发现:地方政府债务管理的两阶段改革都对地方财政可持续性具有显著的正向提升影响;从作用机制来看,“开前门”和“关后门”阶段改革分别通过财政收支巩固和债务规模控制来发挥对地方财政可持续性的有效作用;异质性分析来看,地方政府举债行为激励较弱和财政监管力度较高的地方,政府债务预算硬约束对地方财政可持续性的正向效应会更加显著。研究结果为推进债务预算硬约束来防范地方政府债务风险和提升地方财政可持续性提供了政策启示。

一种基于LC-BoVW模型的建筑垃圾物料识别算法

针对建筑垃圾物料的种类多、形貌易混淆等问题,构建了一种基于局部约束的视觉词袋(local constraint-bag of visual words,LC-BoVW)模型的建筑垃圾物料识别算法。首先,对建筑垃圾物料图像分块,分别提取局部颜色特征和局部二值模式特征;考虑到图像分块特征的局部相似特性,构建LC-BoVW模型分别对目标图像的显著特征进行统计。然后,基于信息融合思想对特征统计量进行融合,形成图像的判别性特征并输入到分类器中进行物料的精确识别。最后,利用自建的5类建筑垃圾物料图像数据集进行实验,实验结果表明,所提算法能够快速有效地实现建筑垃圾物料识别,平均识别准确率可达到97.92%。

Emergency Local Searching Approach for Job Shop Scheduling

Existing methods of local search mostly focus on how to reach optimal solution.However,in some emergency situations,search time is the hard constraint for job shop scheduling problem while optimal solution is not necessary.In this situation,the existing method of local search is not fast enough.This paper presents an emergency local search(ELS) approach which can reach feasible and nearly optimal solution in limited search time.The ELS approach is desirable for the aforementioned emergency situations where search time is limited and a nearly optimal solution is sufficient,which consists of three phases.Firstly,in order to reach a feasible and nearly optimal solution,infeasible solutions are repaired and a repair technique named group repair is proposed.Secondly,in order to save time,the amount of local search moves need to be reduced and this is achieved by a quickly search method named critical path search(CPS).Finally,CPS sometimes stops at a solution far from the optimal one.In order to jump out the search dilemma of CPS,a jump technique based on critical part is used to improve CPS.Furthermore,the schedule system based on ELS has been developed and experiments based on this system completed on the computer of Intel Pentium(R) 2.93 GHz.The experimental result shows that the optimal solutions of small scale instances are reached in 2 s,and the nearly optimal solutions of large scale instances are reached in 4 s.The proposed ELS approach can stably reach nearly optimal solutions with manageable search time,and can be applied on some emergency situations.

基于降维字典学习的高维数据分类策略

为了解决字典学习中的高维数据与非线性问题,提出一种基于降维字典学习的高维数据分类策略。在降维阶段,利用自编码器学习一种非线性映射,该映射可以降维并保留高维数据的非线性结构;在字典学习阶段,利用标签嵌入进行局部约束;在学习过程中,保留了可分解的非线性局部结构,增强了类的区分能力,同时优化了映射函数和字典。在多个基准数据集上的实验结果表明,提出的方法能够有效解决字典学习中的高维数据与非线性问题。

LOCAL BENDING OF THIN FILM ON VISCOUS LAYER

Effects of deposition layer position film are systematically investigated. Because the and number/density on local bending of a thin deposition layer interacts with the thin film at the interface and there is an offset between the thin film neutral surface and the interface, the deposition layer generates not only axial stress but also bending moment. The bending moment induces an instant out-of-plane deflection of the thin film, which may or may not cause the socalled local bending. The deposition layer is modeled as a local stressor, whose location and density are demonstrated to be vital to the occurrence of local bending. The thin film rests on a viscous layer, which is governed by the Navier-Stokes equation and behaves like an elastic foundation to exert transverse forces on the thin film. The unknown feature of the axial constraint force makes the governing equation highly nonlinear even for the small deflection chse. The constraint force and film transverse deflection are solved iteratively through the governing equation and the displacement constraint equation of immovable edges. This research shows that in some special cases, the deposition density increase does not necessarily reduce the local bending. By comparing the thin film deflections of different deposition numbers and positions, we also present the guideline of strengthening or suppressing the local bending.

基于Delta-Debugging的列车运行控制系统故障定位方法研究

当采用组合测试方法对列车运行控制系统定位故障时,为了使系统不受输入参数之间约束关系的影响,准确地定位出致使系统故障的参数组合,以智能高速铁路临时限速场景为例,提出一种采用自适应组合测试的列车运行控制系统故障定位方法。依据临时限速场景的特点,建立组合测试模型,设计自适应组合测试流程;根据组合测试结果,运用Delta-Debugging算法,生成附加测试用例;利用失败测试用例与通过测试用例间的差异生成故障关注模式,在附加测试用例和故障关注模式中使用约束处理策略,最终定位出最小故障模式;通过京张智能高速铁路列车运行控制系统仿真测试平台,验证所提方法的有效性。试验结果表明:当列车运行控制系统存在约束情况时,2-way和4-way覆盖表生成的附加测试用例数量最多可降低24%和27%;相较于传统故障定位方法,所提方法的综合评价指标达95%。该研究成果提高了高速铁路列车运行控制系统相关功能测试的效率及故障定位的准确性。

改进蚁群算法的送餐机器人路径规划

蚁群算法拥有良好的全局性、自组织性、鲁棒性,但传统蚁群算法存在许多不足之处。为此,针对算法在路径规划问题中的缺陷,在传统蚁群算法的状态转移公式中,引入目标点距离因素和引导素,加快算法收敛性和改善局部最优缺陷。在带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)上,融合蚁群算法和遗传算法,并将顾客时间窗宽度以及机器人等待时间加入蚁群算法状态转移公式中,以及将蚁群算法的解作为遗传算法的初始种群,提高遗传算法的初始解质量,然后进行编码,设置违反时间窗约束和载重量的惩罚函数和适应度函数,在传统遗传算法的交叉、变异操作后加入了破坏-修复基因的操作来优化每一代新解的质量,在Solomon Benchmark算例上进行仿真,对比算法改进前后的最优解,验证算法可行性。最后在餐厅送餐问题中把带有障碍物的仿真环境路径规划问题和VRPTW问题结合,使用改进后的算法解决餐厅环境下送餐机器人对顾客服务配送问题。

地方金融监管权力法治化的反思与重塑

地方金融监管权力的法治化塑造,不仅旨在保证地方金融监管权力来源的法定,更关注法治对地方金融监管权力运行进行制约与监督的规范效用,而这需要借助职权法定、行政自制和理性制衡三者协同完成。为回应中央与地方金融监管权责失衡之实践异化,地方金融监管权力法治化的核心要义便是地方金融监管权责的对等化设计,具体是以职权与职责在量上的实质均衡为内核,来完成主体维度中央和地方关系的均衡处理与事项维度规则制定和监管执法的分项设计。当然,地方金融监管权力法治化的实践展开,应当借助地方金融监管权责事项的清单化管理展开,即分别按照“以权定责”和“以责定权”的逻辑来设置权力清单与责任清单各自不同的要素构成,并在“属地管理”观念的统一指引下完成“以块统条”和“以条带块”管理模式的差别化设计。

预算软约束对地区经济增长的影响及其门槛效应研究

在内外需求日益疲软以及地方债务规模较大的情况下,我国推出了一系列积极的财政措施,这要求政府增加举债规模。本研究从全国范围和东、中、西部地区出发,使用基准回归模型和门槛效应模型对地方政府预算软约束对经济增长的影响进行了实证检验。研究发现,地方政府财政预算软约束显著阻碍了地区经济增长,且二者之间存在显著的门槛效应。异质性分析表明,地方政府预算软约束对经济增长的负向作用会受到区域差异的影响。因此,地方政府应采取优化转移支付制度、促进融资平台的市场化转型、完善过度负债约束机制、转变官员政绩考核理念、因地制宜地制定差异性防范政策等措施稳定地区经济增长。

复杂环境下基于自适应极线约束的AGV视觉SLAM算法

针对传统视觉同步定位与地图构建(SLAM)算法不能有效处理复杂环境中的动态及潜在动态目标而影响定位与建图性能的问题,提出一种基于Mask R-CNN神经网络以及ORB-SLAM3算法改进的视觉SLAM方法。针对动态目标,提出一种基于语义信息的运动一致性检验算法,使用自适应阈值的极线约束方法实现图像中动态特征点的精确剔除;针对潜在动态目标,提出一种改进的长期数据关联方法,通过增大关键帧选取密度及优化关键帧中的潜在动态目标信息,对算法的回环优化和地图融合过程进行改进,提高回环优化效果与地图复用性。在TUM数据集和真实场景中进行验证,实验结果表明与ORB-SLAM3算法相比,采用TUM数据集在低动态场景和高动态场景中的绝对轨迹均方根误差分别减小8.5%和65.6%;在真实场景下测试,所提算法的定位精度提高了62.5%。

金融分权影响民营企业融资约束的途径:一个补充讨论

从中国金融分权实施形式出发,分析地方银行作为金融分权的具体机构对民营企业融资约束的影响。首先,地方银行的建立确实会增加民营企业的信贷比率,但存在显著的地区差异,在经济发达、地方国有企业积极推进产业升级的地区这种促进作用更为显著。进一步研究发现,在切实缓解民营企业融资约束方面,重要的不是地方银行规模增加而是银行控制权结构优化,由地方政府向市场控制分权越多,越有利于缓解民营企业融资约束。从城市商业银行入手详细探讨银行控制权变化对其信贷倾向的影响,发现随着地方政府的退出和民营股权的增加,城市商业银行信贷中的民营企业信贷占比会显著增加。但是,并非地方政府退出程度越高越好,在发达地区地方政府过度退出反而不利;相反,在欠发达地区地方政府需要更大程度地退出才能有效促进城市商业银行为民营企业融资。因此,在适当扩大地方银行规模的基础上,优化其控制权结构和加快地方产业升级是缓解民营企业融资约束的关键之举。

地方政府间环境约束目标竞争:理论分析与实证检验

地方政府间协同治理成为中国经济高质量发展的重要环节。论文通过整理275个地级市的政府工作报告中关于环境治理目标约束的内容,将其分为“硬约束”和“软约束”两类;构建地方政府间的演化博弈模型分析其策略行为,研究发现:地方政府选择环境治理目标约束的类型与邻近地方政府的政策选择相关,由周边地区污染示范外部效应以及自身经济增长偏好所决定,并且受地区间的经济规模差距的影响。进一步,利用2006—2018年中国275个地级市的数据检验假设中所描述的情况,回归结果显示假设中分析的各种策略都得到验证;分区域的检验,总体上满足假设,对个别在统计上不显著的结论提供了合理的解释。最后,针对前文分析提出了相关的政策建议。

面向无监督特征提取的结构化图嵌入

特征提取是处理高维数据的最有效工具之一。然而,当前特征提取方法存在两个问题:一是它们没有同时捕捉数据的局部和全局结构;二是构建的图脱离数据的聚类数,没有与聚类相同的连通分量。为了解决这些问题,提出了面向无监督特征提取的结构化图嵌入方法(structured graph embedding, SGE)。通过构建数据表征的K近邻和使用最小二乘回归,SGE能够同时保持数据的局部与全局相关结构。而且,SGE对表征图的拉普拉斯矩阵施加秩约束,使构建的最优图具有与c个聚类一致的c个连通分量,从而能揭示数据的聚类结构。因此,SGE能够找到更有判别力的投影。在多个真实数据集的实验表明,SGE优于其他主流降维方法。特别是在PIE数据集上,SGE的聚类精度比LRPP_GRR的聚类精度高出18.7%。这些结果表明SGE方法可以有效降低数据维数。